Recognising Intrinsic Motivation using Smartphone TrajectoriesCitation formats

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Recognising Intrinsic Motivation using Smartphone Trajectories. / Ibrahim, Ahmed; Clinch, Sarah; Harper, Simon.

In: International Journal of Human-Computer Studies, Vol. 153, 102650, 18.04.2021.

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Ibrahim, Ahmed ; Clinch, Sarah ; Harper, Simon. / Recognising Intrinsic Motivation using Smartphone Trajectories. In: International Journal of Human-Computer Studies. 2021 ; Vol. 153.

Bibtex

@article{927d1cf30269466694feaccd2ff3a335,
title = "Recognising Intrinsic Motivation using Smartphone Trajectories",
abstract = "Human behaviours that are motivated by and indicative of personal interests can be utilised to personalise behavioural recommendations used to promote health and well-being. Behavioural and psychological studies show that (1) personal interests are demonstrated differently in individuals{\textquoteright} daily activities; and (2) drawbacks of self-reporting methods, such as forgetfulness and providing socially accepted answers rather than actual ones, may negatively impact the reliability and validity of the recognition process. To address these two challenges, we propose an adaptive approach that infers personal interests from continuously- and passively-sensed smartphones location data. We evaluate our approach based on two longitudinal datasets gathered by human participants going about their normal daily activities. Our results indicate that our approach successfully identifies interests consistent with those reported by participants, matching or outperforming alternative approaches. We also see high inter-personal variation, suggesting a future role for personalisation in our approach.",
keywords = "Digital phenotype, Interest recognition, Maslow's hierarchy, Self-determination theory, Smartphones, behavioural recommendations",
author = "Ahmed Ibrahim and Sarah Clinch and Simon Harper",
year = "2021",
month = apr,
day = "18",
doi = "10.1016/j.ijhcs.2021.102650",
language = "English",
volume = "153",
journal = "International Journal of Human Computer Studies",
issn = "1071-5819",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Recognising Intrinsic Motivation using Smartphone Trajectories

AU - Ibrahim, Ahmed

AU - Clinch, Sarah

AU - Harper, Simon

PY - 2021/4/18

Y1 - 2021/4/18

N2 - Human behaviours that are motivated by and indicative of personal interests can be utilised to personalise behavioural recommendations used to promote health and well-being. Behavioural and psychological studies show that (1) personal interests are demonstrated differently in individuals’ daily activities; and (2) drawbacks of self-reporting methods, such as forgetfulness and providing socially accepted answers rather than actual ones, may negatively impact the reliability and validity of the recognition process. To address these two challenges, we propose an adaptive approach that infers personal interests from continuously- and passively-sensed smartphones location data. We evaluate our approach based on two longitudinal datasets gathered by human participants going about their normal daily activities. Our results indicate that our approach successfully identifies interests consistent with those reported by participants, matching or outperforming alternative approaches. We also see high inter-personal variation, suggesting a future role for personalisation in our approach.

AB - Human behaviours that are motivated by and indicative of personal interests can be utilised to personalise behavioural recommendations used to promote health and well-being. Behavioural and psychological studies show that (1) personal interests are demonstrated differently in individuals’ daily activities; and (2) drawbacks of self-reporting methods, such as forgetfulness and providing socially accepted answers rather than actual ones, may negatively impact the reliability and validity of the recognition process. To address these two challenges, we propose an adaptive approach that infers personal interests from continuously- and passively-sensed smartphones location data. We evaluate our approach based on two longitudinal datasets gathered by human participants going about their normal daily activities. Our results indicate that our approach successfully identifies interests consistent with those reported by participants, matching or outperforming alternative approaches. We also see high inter-personal variation, suggesting a future role for personalisation in our approach.

KW - Digital phenotype

KW - Interest recognition

KW - Maslow's hierarchy

KW - Self-determination theory

KW - Smartphones

KW - behavioural recommendations

U2 - 10.1016/j.ijhcs.2021.102650

DO - 10.1016/j.ijhcs.2021.102650

M3 - Article

VL - 153

JO - International Journal of Human Computer Studies

JF - International Journal of Human Computer Studies

SN - 1071-5819

M1 - 102650

ER -